Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices

Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensi...
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Gespeichert in:

Autor*in:	Gonzalo Farias [verfasserIn] Ernesto Fabregas [verfasserIn] Sebastian Dormido-Canto [verfasserIn] Jesus Vega [verfasserIn] Sebastian Vergara [verfasserIn] Sebastian Dormido Bencomo [verfasserIn] Ignacio Pastor [verfasserIn] Alvaro Olmedo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Images classification auto-encoder future extraction nuclear fusion

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 6(2018), Seite 72345-72356
Übergeordnetes Werk:	volume:6 ; year:2018 ; pages:72345-72356

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2018.2881832

Katalog-ID:	DOAJ069554099

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allfields	10.1109/ACCESS.2018.2881832 doi (DE-627)DOAJ069554099 (DE-599)DOAJ9f5802444ce54d75989d71d35a924152 DE-627 ger DE-627 rakwb eng TK1-9971 Gonzalo Farias verfasserin aut Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensionality of the problem, making easier any subsequent process such as classification. The main contribution of this research is to evaluate the use of AEs for automatic feature extraction in massive thermonuclear fusion databases. In order to show the performance of AEs in a practical way, the problem of image classification of the TJ-II Thomson Scattering diagnostic has been selected. The classification has been performed by the algorithm of support vector machines and conformal predictors. The results show that the use of AEs produces the predictions faster, with more reliable models, and with higher success rates in comparison to the performance without using the deep learning approach. Images classification auto-encoder future extraction nuclear fusion Electrical engineering. Electronics. Nuclear engineering Ernesto Fabregas verfasserin aut Sebastian Dormido-Canto verfasserin aut Jesus Vega verfasserin aut Sebastian Vergara verfasserin aut Sebastian Dormido Bencomo verfasserin aut Ignacio Pastor verfasserin aut Alvaro Olmedo verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 72345-72356 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:72345-72356 https://doi.org/10.1109/ACCESS.2018.2881832 kostenfrei https://doaj.org/article/9f5802444ce54d75989d71d35a924152 kostenfrei https://ieeexplore.ieee.org/document/8537905/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 72345-72356
spelling	10.1109/ACCESS.2018.2881832 doi (DE-627)DOAJ069554099 (DE-599)DOAJ9f5802444ce54d75989d71d35a924152 DE-627 ger DE-627 rakwb eng TK1-9971 Gonzalo Farias verfasserin aut Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensionality of the problem, making easier any subsequent process such as classification. The main contribution of this research is to evaluate the use of AEs for automatic feature extraction in massive thermonuclear fusion databases. In order to show the performance of AEs in a practical way, the problem of image classification of the TJ-II Thomson Scattering diagnostic has been selected. The classification has been performed by the algorithm of support vector machines and conformal predictors. The results show that the use of AEs produces the predictions faster, with more reliable models, and with higher success rates in comparison to the performance without using the deep learning approach. Images classification auto-encoder future extraction nuclear fusion Electrical engineering. Electronics. Nuclear engineering Ernesto Fabregas verfasserin aut Sebastian Dormido-Canto verfasserin aut Jesus Vega verfasserin aut Sebastian Vergara verfasserin aut Sebastian Dormido Bencomo verfasserin aut Ignacio Pastor verfasserin aut Alvaro Olmedo verfasserin aut In IEEE Access IEEE, 2014 6(2018), Seite 72345-72356 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:6 year:2018 pages:72345-72356 https://doi.org/10.1109/ACCESS.2018.2881832 kostenfrei https://doaj.org/article/9f5802444ce54d75989d71d35a924152 kostenfrei https://ieeexplore.ieee.org/document/8537905/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 72345-72356
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source	In IEEE Access 6(2018), Seite 72345-72356 volume:6 year:2018 pages:72345-72356
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institution	findex.gbv.de
topic_facet	Images classification auto-encoder future extraction nuclear fusion Electrical engineering. Electronics. Nuclear engineering
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container_title	IEEE Access
authorswithroles_txt_mv	Gonzalo Farias @@aut@@ Ernesto Fabregas @@aut@@ Sebastian Dormido-Canto @@aut@@ Jesus Vega @@aut@@ Sebastian Vergara @@aut@@ Sebastian Dormido Bencomo @@aut@@ Ignacio Pastor @@aut@@ Alvaro Olmedo @@aut@@
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callnumber-first	T - Technology
author	Gonzalo Farias
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topic_title	TK1-9971 Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices Images classification auto-encoder future extraction nuclear fusion
topic	misc TK1-9971 misc Images classification misc auto-encoder misc future extraction misc nuclear fusion misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Images classification misc auto-encoder misc future extraction misc nuclear fusion misc Electrical engineering. Electronics. Nuclear engineering
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title	Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices
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title_full	Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices
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title_sort	applying deep learning for improving image classification in nuclear fusion devices
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title_auth	Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices
abstract	Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensionality of the problem, making easier any subsequent process such as classification. The main contribution of this research is to evaluate the use of AEs for automatic feature extraction in massive thermonuclear fusion databases. In order to show the performance of AEs in a practical way, the problem of image classification of the TJ-II Thomson Scattering diagnostic has been selected. The classification has been performed by the algorithm of support vector machines and conformal predictors. The results show that the use of AEs produces the predictions faster, with more reliable models, and with higher success rates in comparison to the performance without using the deep learning approach.
abstractGer	Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensionality of the problem, making easier any subsequent process such as classification. The main contribution of this research is to evaluate the use of AEs for automatic feature extraction in massive thermonuclear fusion databases. In order to show the performance of AEs in a practical way, the problem of image classification of the TJ-II Thomson Scattering diagnostic has been selected. The classification has been performed by the algorithm of support vector machines and conformal predictors. The results show that the use of AEs produces the predictions faster, with more reliable models, and with higher success rates in comparison to the performance without using the deep learning approach.
abstract_unstemmed	Deep learning has become one of the most promising approaches in recent years. One of the main applications of deep learning is the automatic feature extraction with auto-encoders (AEs). Feature extraction, one of the most important stages in machine learning, that can reduce drastically the dimensionality of the problem, making easier any subsequent process such as classification. The main contribution of this research is to evaluate the use of AEs for automatic feature extraction in massive thermonuclear fusion databases. In order to show the performance of AEs in a practical way, the problem of image classification of the TJ-II Thomson Scattering diagnostic has been selected. The classification has been performed by the algorithm of support vector machines and conformal predictors. The results show that the use of AEs produces the predictions faster, with more reliable models, and with higher success rates in comparison to the performance without using the deep learning approach.
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title_short	Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices
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